Psychoradiology最新文献

Background: Although sex differences in antisocial behavior are well-documented, the extent to which neuroanatomical differences are related to sex differences in antisocial behavior is unclear. The inconsistent results from different clinical populations exhibiting antisocial behaviors are mainly due to the heterogeneity in etiologies, comorbidity inequality, and small sample size, especially in females.

Objective: The study aimed to find sexual dimorphic brain regions associated with individual differences in antisocial behavior while avoiding the issues of heterogeneity and sample size.

Methods: We collected structural neuroimaging data from 281 college students (131 males, 150 females) and analyzed the data using voxel-based morphometry.

Results: The gray matter volume in three brain regions correlates with self-reported antisocial behavior in males and females differently: the posterior superior temporal sulcus, middle temporal gyrus, and precuneus. The findings have controlled for the total cortical gray matter volume, age, IQ, and socioeconomic status. Additionally, we found a common neural substrate of antisocial behavior in both males and females, extending from the anterior temporal lobe to the insula.

Conclusion: This is the first neuroanatomical evidence from a large non-clinical sample of young adults. The study suggests that differences in males and females in reading social cues, understanding intentions and emotions, and responding to conflicts may contribute to the modulation of brain morphometry concerning antisocial behavior.

Background: Autism spectrum disorder (ASD) is characterized by social and behavioural deficits. Current diagnosis relies on behavioural criteria, but machine learning, particularly connectome-based predictive modelling (CPM), offers the potential to uncover neural biomarkers for ASD.

Objective: This study aims to predict the severity of ASD traits using CPM and explores differences among ASD subtypes, seeking to enhance diagnosis and understanding of ASD.

Methods: Resting-state functional magnetic resonance imaging data from 151 ASD patients were used in the model. CPM with leave-one-out cross-validation was conducted to identify intrinsic neural networks that predict Autism Diagnostic Observation Schedule (ADOS) scores. After the model was constructed, it was applied to independent samples to test its replicability (172 ASD patients) and specificity (36 healthy control participants). Furthermore, we examined the predictive model across different aspects of ASD and in subtypes of ASD to understand the potential mechanisms underlying the results.

Results: The CPM successfully identified negative networks that significantly predicted ADOS total scores [r (df = 150) = 0.19, P = 0.008 in all patients; r (df = 104) = 0.20, P = 0.040 in classic autism] and communication scores [r (df = 150) = 0.22, P = 0.010 in all patients; r (df = 104) = 0.21, P = 0.020 in classic autism]. These results were reproducible across independent databases. The networks were characterized by enhanced inter- and intranetwork connectivity associated with the occipital network (OCC), and the sensorimotor network (SMN) also played important roles.

Conclusions: A CPM based on whole-brain resting-state functional connectivity can predicted the severity of ASD. Large-scale networks, including the OCC and SMN, played important roles in the predictive model. These findings may provide new directions for the diagnosis and intervention of ASD, and maybe could be the targets in novel interventions.

In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.